Image-style-conversion systems convert natural or computer-generated images to style-imitation images resembling a drawing or other artwork with hand-drawn strokes. By creating style-imitation images, conventional image-style-conversion systems attempt to generate outputs reflecting the characteristics of an artistic style in ink, pencil, paint, or other medium. Despite advances in generating style-imitation images, certain technical limitations impede conventional image-style-conversion systems from realistically resembling artistic styles, resembling different artistic styles, or applying a neural network that can accurately and consistently imitate artistic strokes.
For example, some conventional image-style-conversion systems cannot currently render style-imitation images that accurately portray an artistic style. In some cases, for instance, conventional image-style-conversion systems generate images that resemble the global black-and-white depictions of a target-drawing style, but fail to capture a stroke style or other artistic marking of the target-drawing style. Some rendering techniques use gradients for input images to render more visible (or obvious) strokes from a target-pencil-drawing style. But such rendering techniques neither render the more visible strokes in a realistic depiction nor render more subtle strokes. Further, some conventional image-style-conversion systems utilize vector lines from an input image to resemble pencil strokes from a target style, but lack the computer engineering to convert natural images to resemble such pencil strokes.
In addition to technical accuracy and imitation problems, in some cases, conventional image-style-conversion systems can intake only certain types of images or generate style-imitation images resembling a specific artistic style. For instance, some conventional image-style-conversion systems can generate (albeit with flawed realism) a particular pencil style, but only when an input image includes distinct lines and does not include natural photography. Conversely, some conventional image-style-conversion systems can only produce images resembling pencil strokes. Such inflexible models limit the artistic styles imitated (and inputs converted).
Independent of technical accuracy or flexibility limitations, conventional image-style-conversion systems often cannot successfully employ neural networks to imitate an artistic style. To train such neural networks, some conventional image-style-conversion systems rely on training images that correspond to conventional paired-ground-truth drawings. But generating such paired-ground-truth drawings can be labor or time intensive and (in some cases) practically impossible. To produce a reasonable level of accuracy in such conventional systems, a human artist may need to hand draw numerous paired-ground-truth drawings in a time-consuming process. The time and cost of creating such paired-ground-truth drawings can prevent computer engineers from reaching a sample size that enables a neural network to train to a point of accurately imitating an artistic style.